UNLABELLED: A multitude of motif-finding tools have been published, which can generally be assigned to one of three classes: expectation-maximization, Gibbs-sampling or enumeration. Irrespective of this grouping, most motif detection tools only take into account similarities across ungapped sequence regions, possibly causing short motifs located peripherally and in varying distance to a 'core' motif to be missed. We present a new method, adding to the set of expectation-maximization approaches, that permits the use of gapped alignments for motif elucidation. AVAILABILITY: The program is available for download from: http://bioinfoserver.rsbs.anu.edu.au/downloads/mclip.jar. SUPPLEMENTARY INFORMATION: http://bioinfoserver.rsbs.anu.edu.au/utils/mclip/info.php.

UNLABELLED: Analysis of microarray experiments is complicated by the huge amount of data involved. Searching for groups of co-expressed genes is akin to searching for protein families in a database as, in both cases, small subsets of genes with similar features are to be found within vast quantities of data. CLANS was originally developed to find protein families in large sets of amino acid sequences where the amount of data involved made phylogenetic approaches overly cumbersome. We present a number of improvements that greatly extend the previous version of CLANS and show its application to microarray data as well as its ability of incorporating additional information to facilitate interactive analysis. AVAILABILITY: The program is available for download from: http://bioinfoserver.rsbs.anu.edu.au/downloads/clans/

BACKGROUND: To interpret microarray experiments, several ontological analysis tools have been developed. However, current tools are limited to specific organisms. RESULTS: We developed a bioinformatics system to assign the probe set sequences of any organism to a hierarchical functional classification modelled on KEGG ontology. The GeneBins database currently supports the functional classification of expression data from four Affymetrix arrays; Arabidopsis thaliana, Oryza sativa, Glycine max and Medicago truncatula. An online analysis tool to identify relevant functions is also provided. CONCLUSION: GeneBins provides resources to interpret gene expression results from microarray experiments. It is available at http://bioinfoserver.rsbs.anu.edu.au/utils/GeneBins/

MOTIVATION: Based on a gene classification into hierarchical categories ('BINs'), MapMan was originally developed to display Arabidopsis thaliana gene expression in a functional context. We have created a bioinformatics system to extend MapMan to any organism by using a new BIN structure based on the KEGG database. Gene sequences are assigned to this ontology by homology relationships in four reference databases: KEGG, COG, Swiss-Prot and Gene Ontology. We applied this system to tailor MapMan to the GeneChips of two model legumes, Glycine max and Medicago truncatula. We also developed a module to identify the most relevant pathways involved. AVAILABILITY: All mapping files, pathway pictures and the analysis method are available at http://bioinfoserver.rsbs.anu.edu.au/

A package of computer programs called DIPLOMO (DIstance PLOt MOnitor) has been developed for making pairwise comparisons of different estimates of the distances between a set of taxa by plotting them against each other in a simple scatter plot. Taxa with similar relative distance characteristics are thereby grouped graphically. Groupings of different taxa may be directly identified, and the distance characteristics of chosen groups visualised and compared using devices to give them different colours or symbols. The program is particularly useful for detecting and analysing subtle trends in gene sequence evolution. This is done by comparing different components of change, for example synonymous versus non-synonymous nucleotide changes, transversions versus transitions and changes in different genes of the same set of taxa, etc. The program has a wide range of other uses, for example comparing different methods of sequence analysis, assessing which components of genetic change correlate best with phenotypic change or with geographical separation. This paper describes the DIPLOMO package, and illustrates typical DIPLOMO analyses using lentivirus gene sequence data.